yl6809永利官网学术讲座

时间：9月22日下午16:30-17:20

地点：学院210会议室

题目一：Data-driven text adaptation for preparing testing materials: What should teachers learn?

演讲人：金檀博士

摘 要

  While a large body of corpus-based studies have provided valuable insights to inform the content of testing materials, direct explorations of corpora by teachers to adapt source texts (i.e., data-driven text adaptation) for testing material preparation remain a largely unexplored area. In this connection, this presentation reports on a project that examines the practice of teachers in the production of testing materials, focusing on the knowledge and skills needed when performing data-driven text adaptation. The data-driven text adaptation approach was implemented through an online data-driven tool for English text adaptation, Eng-Editor. A qualitative case study, composed of three stages, was undertaken to investigate the types of knowledge and skills teachers needed to perform data-driven text adaptation. First, semi-structured interviews with a group of six teachers were conducted to elicit their perceptions of the knowledge and skills needed. Second, based on the interviews, an online professional development (PD) course was designed and offered to a group of approximately 200 teachers. Third, post-hoc online queries as well as written reflections by the teachers in the PD course were analyzed to gain additional insight into teachers’ learning needs. Overall, the study provides strong evidence that teachers can learn and benefit from the data-driven text adaptation approach. The study also highlights that certain knowledge and skills are needed to yield the best results of the data-driven adapting actions by teachers. Implications are also given to further develop the assessment literacy of teachers in human-machine collaboration for producing testing materials.

主讲人简介

Tan Jin, PhD

School of Foreign Languages, Sun Yat-sen University

  Tan’s research interests include language testing, data mining as well as data-driven learning. He has over 30 publications in refereed journals and conferences, including Language Testing, TESOL Quarterly, TKDE, Information Sciences, WWW and ICDE. Tan created the first confidence scoring approach based on fuzzy logic to measure candidate performances for testing speaking (Language Testing, 2012) – an output listed in the Research Timeline of Assessing Second Language Speaking (Language Teaching, 2015). In addition, he applied data-mining techniques to the development of an online system, Eng-Editor, providing data-driven feedback to item writers for an online testing and assessment system iTEST, which was adopted by over 300 universities in China. In recent years, Tan is providing online data literacy courses to language teachers and testers in China. He co-edited “Language, Data and Research” series with Dr. Xiaofei Lu from Pennsylvania State University – over 10,000 postgraduate students, language teachers as well as academic researchers in applied linguistics participated in the online series of talks, webinars and/or courses. Tan is now leading a project team that aims to implement an online multimodal corpus including a unique collection of Chinese EFL learners’ use of evidence in speaking performance – a project supported by the Humanities and Social Science Foundation, Ministry of Education of China.

题目二：限定量词的语义解读及其句法结构关系

演讲人：黄瓒辉副教授

  提要：据Herburger（1997，2000），在阶段性谓词的语境中，处在限定量词内部论元位置的焦点对弱量化词而不是强量化词的语义解读有影响，而处在VP中的焦点对强量化词和弱量化词的语义解读都有影响。本研究在此基础之上，进一步详细考察处在VP中的焦点对限定量词语义解读的影响；同时考察限定量词句的句法结构与语义结构的关系及其原因。

  结论：当焦点处在VP中时，全称限定量词实际难以对焦点敏感，其他限定量词对焦点敏感，但由于其比例解读和焦点解读的真值存在不同的蕴涵关系，使得其体现出的对FV敏感性的强弱程度不同；限定量词句的语义结构在阶段性谓词的语境中受焦点影响而跟句法结构产生不一致关系，且这种不一致关系强弱量化词有别，原因在于阶段性谓词表示的具体场景中的具体事件，更容易成为参与者成分的标志而起到帮助辨识参与者的作用；弱量化词的强述谓性和强量化词的话题性，使得其内部论元位置的焦点成分在是否能移到LF层的VP中有别。

演讲人简介：

  黄瓒辉，2004年毕业于北京大学中文系，获汉语言文字学博士学位。2006年至2009年于香港理工大学从事博士后研究工作。2016年-2017年于美国威斯康辛大学麦迪逊校区访问。主要从事现代汉语句法、语义的研究。有数篇论文在《中国语文》、《当代语言学》、《世界汉语教学》等期刊上发表。近期研究兴趣在汉语焦点、量化、集合义表达等问题。曾主持国家社科基金青年项目“现代汉语事件量化系统的研究”。目前有教育部人文社科青年基金项目“现代汉语集合性谓词的研究”和国家社科基金一般项目“聚合义词汇语法表达的类型学比较研究”在研。